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* test(plugins): capture opt-in artifact RED (18 failed, 13 passed) * feat(plugins): add typed opt-in artifacts (31 ToolNode tests pass) * test(extensions): require API 0.2.6 (version RED verified) * build(extensions): pin API 0.2.6 (installed wheel guards GREEN) * test(examples): verify plugin artifacts through checkpoint restart * test(extensions): reproduce artifact key coercion (8 RED cases) * fix(extensions): reject non-string artifact keys (185 tests GREEN) * fix(frontend): list bundled retrieval example in extension catalog * test(mcp): reconcile recovered notification at stored due time --------- Co-authored-by: Totoro-qaq <279883115+Totoro-qaq@users.noreply.github.com>
59 lines
2.5 KiB
Markdown
59 lines
2.5 KiB
Markdown
# Synthetic retrieval with plugin tool artifacts
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This is a small, read-only example for `ModelTool(response_format="content_and_artifact")`.
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It searches two fixed **synthetic** documents in memory. It makes no network or
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model requests, reads no user files, and has no UI, database, ingestion, or write
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operations. All users see the same demonstration corpus; it is not a private
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knowledge base or a factual company source.
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The package imports only Python's standard library and `deerflow-extension-api`
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0.2.6 or newer. It does not import private DeerFlow harness modules.
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## Enable explicitly
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Install this trusted local package using the normal extension workflow:
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```sh
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deerflow extensions install /absolute/path/to/examples/deerflow-extension-retrieval
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```
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The contribution is disabled by default. To enable it, set the installed entry's
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configuration to `enabled: true` and restart the Gateway. For a source package
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already on the Python path, the equivalent deployment entry is:
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```yaml
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plugins:
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- use: deerflow_extension_retrieval:install
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config:
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enabled: true
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```
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The registered model tool performs case-insensitive literal matching. For
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example, the query `release` returns the synthetic release note. Empty queries
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and additional arguments are rejected by the tool schema.
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## What the example proves
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The handler returns `PluginToolResult`: readable text is model-visible, while a
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JSON artifact contains `schema`, `synthetic`, and structured `matches`. The host
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assigns the tool name and call ID. The artifact contains no secret information;
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artifacts are retained in state and API output, not a secret-storage boundary.
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The integration test loads this package through `load_extensions`, dispatches
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the real `ToolNode` with `ToolOutputBudgetMiddleware`, saves to SQLite, closes
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and reopens the saver, reads through `CheckpointStateAccessor`, and serializes
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the restored state. A later turn also checks the standard model-message
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conversion receives text rather than the artifact. Both full and delta
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checkpoint modes are covered, including content truncation.
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The artifact is generic example metadata. It does **not** claim to be a native
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RAGFlow citation, enable citation cards, or participate in source-aware budget
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pruning or subagent source forwarding. Generic artifacts can outlive truncated
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content; adding an artifact does not establish evidence authority.
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From an existing DeerFlow backend development environment with API 0.2.6:
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```sh
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uv run pytest tests/test_retrieval_plugin_artifacts.py -q
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```
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